A Framework for Parametric Model Selection in Time Series Problems
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Tarih
2023
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Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
People make future plans with the aim of simplifying their lives, and these plans are essential for preparing for forthcoming challenges. Forecasting methodologies take precedence in order to anticipate and plan for future events. Time series data stands out as a pivotal information type employed for predicting the future. This research introduces a framework for selecting the optimal model among classical artificial neural networks in time series forecasting. The classical artificial neural networks considered encompass the LSTM, CNN, and DNN models. The framework employs various parameters – including the dataset, model depth, loss functions, minimal success rate in model performance, epochs, and optimization algorithms – to determine the best-fitting model. Users have the flexibility to adjust these parameters to address specific issues. By default, the framework incorporates seven distinct loss functions and five optimization algorithms to facilitate model selection. The mean average error loss function is used as the metric for evaluating model performance. To validate the framework, Brent oil prices were utilized as the dataset in a series of tests, encompassing a total of 9000 daily price data points. The dataset was partitioned into 80\\% for training and 20\\% for testing purposes. The training iterations within the framework were 50 epochs. In the test scenarios, the price for the eighth day was predicted using price data from the preceding seven days. Consequently, a mean average error score of 1.1239657 was achieved. The results showed that the LSTM model, comprising two layers, the Adadelta optimization algorithm, and the mean square error loss function, emerged as the most successful configuration.
Açıklama
Anahtar Kelimeler
Bilgisayar Bilimleri, Yazılım Mühendisliği, Fizik, Uygulamalı, Termodinamik, Bilgisayar Bilimleri, Teori ve Metotlar, İstatistik ve Olasılık, Bilgisayar Bilimleri, Yapay Zeka, Forecasting, lstm, Time series, cnn, dnn
Kaynak
Gazi Mühendislik Bilimleri Dergisi
WoS Q Değeri
Scopus Q Değeri
Cilt
9
Sayı
4
